049 Defining the characteristics of pain in osteoarthritis to guide treatments
Notice bibliographique
Résumé
Background: Osteoarthritis (OA) is a debilitating condition involving multiple joint changes, including cartilage degradation, bone marrow lesions (BMLs) and synovitis. OA pain afflicts millions of citizens worldwide and whilst its origins remain obscure, recent studies link pain to the presence, size and frequency of BMLs, cartilage degradation and synovitis. The aim of this study was to evaluate the relationship between clinical features of pain and structural damage in participants with knee OA. Methods: We conducted a longitudinal study in participants with full informed consent. Participants were recruited with differing stages of OA, including mild, moderate and severe changes based on Kellgren-Lawrence grading. Data for pain characteristics, including pain scores by WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) and the Visual Analogue Scale (VAS), pain pressure algometry (PPT) and structural damage by MRI using the MRI Knee Osteoarthritis Score (MOAKS) were used to assess the level of pain and structural damage respectively. Dataset analyses were then performed to evaluate the level of structural damage in relation to pain by clinical outcome scores and PPT. Data was corrected for confounding by age and body mass index (BMI). We measured response to treatment after 12 months using the OARSI (Osteoarthritis Research Society International) response criteria, based on detailed WOMAC and VAS scores obtained in the study. Results: We evaluated a total of 124 participants with knee OA. Of the whole group, 74% had advanced OA requiring a total knee replacement (TKR) and 26% had mild OA requiring medical management. We found that participants undergoing total knee replacement for OA had significantly higher MRI measures of structural damage that included BMLs, synovitis and cartilage damage, than patients undergoing standard medical management (p = 0.009 correcting for age and BMI). Whilst 83% of participants undergoing TKR showed a positive treatment outcome, only 24% who were undergoing medical management showed an improvement with standard treatment, with 36% of participants showing relative stability and 29% showing progression in disease symptoms. Participants undergoing standard medical care had a range of structural damage with total MOAKS scores overlapping with those going for TKR, and the progression in disease symptoms was not related to MOAKS, but was related to sensitisation by PPT (p = 0.025 between responders and non-responders to conventional medical treatment). Conclusion: Our data suggest that it is possible to stratify people into distinct subgroups with knee OA. We found that the majority of participants undergoing knee replacement have a good improvement after surgery. However, subjects undergoing medical management have a poorer outcome when they displayed features of pain sensitisation. Our data suggest that subgrouping of participants by pain sensitisation measures may improve stratification and guide improved therapeutic pathways for osteoarthritis. Disclosures: N. Sofat: Consultancies; NS has performed advisory work for Pfizer. S. Koushesh: None. L. Assi: None. V. Ejindu: None. C. Heron: None. R. Ramsden: None. F. Howe: None.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».